This invention relates generally to pattern recognition, and, more particularly to a system and method for pattern classification.
In many pattern recognition systems, for the system to operate effectively or most efficiently, it is necessary to recognize an input as belonging to one class of inputs among many possible classes of inputs. For example, when addressed objects are scanned and the scanned information is analyzed to determine the content of the addresses, the efficiency of the analysis is greatly enhanced if the information scanned in regions of interest is classified as belonging to a particular type of region of interest.
A classifier is a system that recognizes an input as being a member of one of many possible classes. The theoretical optimum classifier is a Bayes type classifier. A Bayes type classifier computes the conditional probability of different classes given the values of other attributes and selects the class with the highest conditional probability. The a priori determination of the probabilities would require knowledge of all possible inputs to the system. For a real life system, the knowledge of all possible inputs would be a prohibitive task.
Neural networks and genetic algorithms have been used to approximate the system probabilities from a small number of system inputs. For example, in U.S. Pat. No. 6,021,220 (granted to E. J. Anderholm on Feb. 1, 2000), a genetic algorithm is used to generate an approximation to a Bayes type classifier using a small number of system inputs. There are also numerous examples of neural networks utilized to implement classifiers. In an example related to classifying areas of interest in an addressed object, a neural network is utilized in U.S. Pat. No. 6,014,450 (granted to Hellper et al. on Jan. 11, 2000) to classify blocks as ‘TEXT’ or “OTHER’. However, the use of neural networks and genetic algorithms results in classifiers that are complex and not as suitable for real time use.
There is a need for a simple classifier suitable for real time use. There is also a need for a classifier that adapts to the changing system conditions and does not require recalculating the details of the classifier.